Enhanced program guide

• US 10,097,897 B2
• Filed: 03/10/2016
• Issued: 10/09/2018
• Est. Priority Date: 06/17/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Funk, Gregory Allan, Drochomirecki, Przemek, Glowaty, Grzegorz, Surazski, Jacek Jakub
• Primary Examiner(s): Chin, Ricky
• Application Number:
  US15/066,614
• Publication Number:
  US 20160198230A1
• Time in Patent Office: 943 Days
• Field of Search: None
• CPC Class Codes: H04N 21/23109 by placing content in organized collections, e.g. EPG data repository details of retrieval of video data and associated meta data in video databases G06F16/739
• CPC Class Codes: H04N 21/232 Content retrieval operation locally within server, e.g. reading video streams from disk arrays storage management G06F3/0604; details of querying and searching of video data from a database G06F16/739
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/432 Content retrieval operation from a local storage medium, e.g. hard-disk details of retrieval of video data and associated meta data in video databases G06F16/739
• CPC Class Codes: H04N 21/4332 by placing content in organized collections, e.g. local EPG data repository interfaces, Database management systems or updating for information retrieval G06F16/23; details of retrieval of video data and associated meta data in video database G06F16/739
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

Systems and methods described herein relate to an enhanced program guide for programs that are broadcast according to a defined schedule. Program titles included in the guide can be ordered based on a relevance rank or score, potentially with no other parameter employed in determining a position of a program title within the guide. Presentation of the guide can be independent of a time axis or dimension and a channel axis or dimension. Titles can be displayed in a manner that is independent of a start time or running length of the associated program.

Claims

1. A system for media guidance, the system comprising:

a memory that stores computer executable components; and

a hardware processor that, when executing the computer executable components stored in the memory, is configured to;

receive program schedule data for a plurality of channels that broadcast a plurality of programs, wherein the program schedule data includes, for each of the plurality of channels, title information of a program being broadcast on a channel and time information associated with the broadcast of the program;

receive a request to present a program guide on a user device;

cause a program guide to be presented in response to the request;

subsequent to presenting the program guide, detect a viewing style from a plurality of viewing styles based on content types associated with previously viewed programs;

determine, from the user device, a plurality of relevance factors based on the previously viewed programs that include a time adjustment value based on the time remaining in the broadcast of the program, a language adjustment value, and a popularity adjustment value, wherein the language adjustment value is determined by determining a location of the user device using one or more Internet protocol geolocation techniques and determining the language adjustment value for adjusting relevance scores based on the determined location;

determine a relevance score associated with each program from the plurality of programs based on the detected viewing style, wherein the relevance score is determined by selecting a ranking model for each of the plurality of relevance factors from a plurality of ranking models based on a type of relevance factor received from the user device, applying each of the selected ranking models to the corresponding relevance factor in which a first output from a first ranking model indicates a likelihood that a user of the user device would watch each program based on the determined time adjustment value, a second output from a second ranking model indicates a likelihood that the user of the user device would watch each program presented in a particular language based on the determined language adjustment value, and a third output from a third ranking model indicates a likelihood that the user of the user device would watch each program having a particular viewership based on the determined popularity adjustment value, and combining outputs from each of the selected ranking models to generate the relevance score;

generate a modified program guide with a subset of programs from the plurality of programs selected based on the relevance score and based on the detected viewing style, wherein the subset of programs is positioned in the program guide based on the relevance score and independent of the channel and the time information received with the program schedule data; and

cause the program guide being presented to be replaced with the modified program guide.

2. The system of claim 1, wherein presentation of the program guide is performed in manner that is independent of a channel axis associated with the channel and a time axis associated with the time of the broadcast.

3. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a time adjustment determined from a comparison of a current time to a start time or a remaining time for each program.

4. The system of claim 3, wherein the hardware processor is further configured to determine the time adjustment as a function of a genre or a category associated with each program, wherein the time adjustment influences the relevance score more significantly for a first genre or category than for a second genre or category.

5. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a language adjustment determined based on data associated with a user device.

6. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a media content quality adjustment determined based on data associated with a user device.

7. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a personalization adjustment determined based on data associated with a user device or a user, and a ranking model.

8. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a live broadcast popularity adjustment determined based on data associated with the program.

9. The system of claim 1, wherein the hardware processor is further configured to determine the relevance score based on a daypart adjustment determined based on a calendar time associated with the broadcast of the program.

10. A method for media guidance, the method comprising:

receiving, using a hardware processor, program schedule data for a plurality of channels that broadcast a plurality of programs, wherein the program schedule data includes, for each of the plurality of channels, title information of a program being broadcast on a channel and time information associated with the broadcast of the program;

receiving, using the hardware processor, a request to present a program guide on a user device;

causing, using the hardware processor, a program guide to be presented in response to the request;

subsequent to presenting the program guide, detecting, using the hardware processor, a viewing style from a plurality of viewing styles based on content types associated with previously viewed programs;

determining, using the hardware processor, a plurality of relevance factors based on the previously viewed programs that include a time adjustment value based on the time remaining in the broadcast of the program, a language adjustment value, and a popularity adjustment value, wherein the language adjustment value is determined by determining a location of the user device using one or more Internet protocol geolocation techniques and determining the language adjustment value for adjusting relevance scores based on the determined location;

determining, using the hardware processor, a relevance score associated with each program from the plurality of programs based on the detected viewing style, wherein the relevance score is determined by selecting a ranking model for each of the plurality of relevance factors from a plurality of ranking models based on a type of relevance factor received from the user device, applying each of the selected ranking models to the corresponding relevance factor in which a first output from a first ranking model indicates a likelihood that a user of the user device would watch each program based on the determined time adjustment value, a second output from a second ranking model indicates a likelihood that the user of the user device would watch each program presented in a particular language based on the determined language adjustment value, and a third output from a third ranking model indicates a likelihood that the user of the user device would watch each program having a particular viewership based on the determined popularity adjustment value, and combining outputs from each of the selected ranking models to generate the relevance score;

generating, using the hardware processor, a modified program guide with a subset of programs from the plurality of programs selected based on the relevance score and based on the detected viewing style, wherein the subset of programs is positioned in the program guide based on the relevance score and independent of the channel and the time information received with the program schedule data; and

causing, using the hardware processor, the program guide being presented to be replaced with the modified program guide.

11. The method of claim 10, wherein presentation of the program guide is performed in manner that is independent of a channel axis associated with the channel and a time axis associated with the time of the broadcast.

12. The method of claim 10, wherein the relevance score is determined based on a time adjustment determined from a comparison of a current time to a start time or a remaining time for each program.

13. The method of claim 12, wherein the time adjustment is determined as a function of a genre or a category associated with each program, and wherein the time adjustment influences the relevance score more significantly for a first genre or category than for a second genre or category.

14. The method of claim 10, wherein the relevance score is determined based on a language adjustment determined based on data associated with a user device.

15. The method of claim 10, wherein the relevance score is determined based on a media content quality adjustment determined based on data associated with a user device.

16. The method of claim 10, wherein the relevance score is determined based on a personalization adjustment determined based on data associated with a user device or a user, and a ranking model.

17. The method of claim 10, wherein the relevance score is determined based on a live broadcast popularity adjustment determined based on data associated with the program.

18. The method of claim 10, wherein the relevance score is determined based on a daypart adjustment determined based on a calendar time associated with the broadcast of the program.